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Principal Investigator: Aresha Maree Martinez-Cardoso
Organization: UNIVERSITY OF CHICAGO
Fiscal Year: 2024
Award: $126,486
Funding agency: National Institute on Minority Health and Health Disparities
Abstract
The United States ranks among the worst of all developed countries in perinatal health, including high rates of
maternal and infant mortality and pervasive disparities among women who are racial/ethnic minorities, have
lower SES, and reside in rural regions. Perinatal outcomes are influenced by an interplay of clinical, individual,
and social factors, but the role of social and structural determinants has been increasingly investigated as a
driver of maternal and child health (MCH) disparities. Social determinants of health (SDOH), the social,
economic, and physical conditions outside the medical system that shape health, have been widely explored
particularly through a multitude of place-based SDOH measures. However, these measures fail to
comprehensively capture the structural drivers of MCH- the policies, macroeconomic forces, institutions and
systems, and culture that generate socioeconomic inequalities across places and populations. In addition,
despite evidence that structural drivers vary widely across regions in the US and that the health of
marginalized groups is more sensitive to place-based factors, the geospatial patterning of structural drivers and
their association with MCH disparities is also poorly understood. Understanding the spatial patterning of
structural drivers and their association to MCH disparities would inform the development of tailored, place-
based, policies and multilevel interventions. The candidate, Dr. Martinez-Cardoso, is applying for this K01
award in order to develop advanced methodological training to address these research gaps. Dr. Martinez-
Cardoso is a well-trained public health researcher with complementary expertise in quantitative data analysis
and health disparities. The training component of the award includes formal/informal training in big data
science, geospatial analytics, and causal inference, paired with a high-caliber mentor and advisory committee.
This training will be applied to research characterizing county-level typologies of structural drivers using data
science and machine-learning approaches (Aim1). Aim 2 will investigate the association between structural
drivers and racial/ethnic health disparities among women of reproductive age using causal inference methods
and multilevel modeling. Aim 3 will explore associations between structural drivers and racial/ethnic perinatal
health disparities using spatial multilevel models. Ultimately, this research seeks to contribute to a
comprehensive understanding of the structural drivers shaping MCH outcomes to effectively reduce disparities
and promote equitable MCH across diverse geographic regions and racial/ethnic groups in the United States.
The award will also catalyze the candidate’s long-term goal of becoming an independent investigator focused
on improving MCH using novel data science tools and innovative multilevel interventions and policies.
Terms: <Address><Advisory Committees><Age><Area><Award><Birth><Birth Records><COVID-19><COVID-19 associated death><COVID-19 associated fatality><COVID-19 associated mortality><COVID-19 death><COVID-19 fatality><COVID-19 induced death><COVID-19 induced fatality><COVID-19 induced mortality><COVID-19 mortality><COVID-19 related death><COVID-19 related fatality><COVID-19 related mortality><COVID19 associated death><COVID19 associated fatality><COVID19 associated mortality><COVID19 death><COVID19 fatality><COVID19 induced death><COVID19 induced fatality><COVID19 induced mortality><COVID19 mortality><COVID19 related death><COVID19 related fatality><COVID19 related mortality><CV-19><Caliber><Censuses><Classification><Clinical><Communities><Conceptions><Coronavirus Infectious Disease 2019><County><Data><Data Analyses><Data Analysis><Data Analytics><Data Science><Data Set><Decrease disparity><Developed Countries><Development><Dimensions><Disparities><Disparity><Economics><Education><Educational aspects><Equity><Ethnic Group><Ethnic Origin><Ethnic People><Ethnic Population><Ethnic individual><Ethnicity><Ethnicity People><Ethnicity Population><Female Health><Generalized Growth><Geographic Area><Geographic Locations><Geographic Region><Geographical Location><Geography><Goals><Growth><Health><Health Inequity><Health Policy><Health Resources><Health behavior><Heterogeneity><Housing><Individual><Industrialized Countries><Industrialized Nations><Inequalities in Health><Inequality><Inequities in Health><Inequity><Infant><Infant Mortality><Infant Mortality Total><Infrastructure><Institution><Intervention><Intervention Strategies><Investigators><Investments><Jobs><K01 Award><K01 Mechanism><K01 Program><Latine><Latinx><Link><Location><Lower disparity><Machine Learning><Maternal Mortality><Maternal and Child Health><Measures><Mediating><Medical><Mentored Research Scientist Development Award><Mentored Training Award><Mentors><Methodology><Methods><Modeling><National Health Interview Survey><Nature><Occupations><Outcome><Parturition><Pattern><Perinatal><Peripartum><Policies><Politics><Population><Professional Positions><Public Health Education><Public Health Instruction><Public Health Training><Race><Races><Racial Group><Research><Research Personnel><Research Scientist Development Award><Researchers><Role><SARS-CoV-2 associated death><SARS-CoV-2 associated fatality><SARS-CoV-2 associated mortality><SARS-CoV-2 death><SARS-CoV-2 fatality><SARS-CoV-2 induced death><SARS-CoV-2 induced fatality><SARS-CoV-2 induced mortality><SARS-CoV-2 mortality><SARS-CoV-2 related death><SARS-CoV-2 related fatality><SARS-CoV-2 related mortality><SES disparity><Schools><Shapes><Socio-economic status><Socioeconomic Status><Stress><System><Systematics><Task Forces><Testing><Tissue Growth><Training><Training Programs><Typology><United States><Vital Statistics><Women's Health><Work><advisory team><ages><analytical tool><big-data science><coronavirus disease 2019><coronavirus disease 2019 associated death><coronavirus disease 2019 associated fatality><coronavirus disease 2019 associated mortality><coronavirus disease 2019 death><coronavirus disease 2019 fatality><coronavirus disease 2019 induced death><coronavirus disease 2019 induced fatality><coronavirus disease 2019 induced mortality><coronavirus disease 2019 mortality><coronavirus disease 2019 related death><coronavirus disease 2019 related fatality><coronavirus disease 2019 related mortality><coronavirus disease-19><coronavirus disease-19 mortality><coronavirus infectious disease-19><data interpretation><death among infants><death due to COVID-19><death due to COVID19><death due to SARS-CoV-2><death due to coronavirus disease 2019><death due to severe acute respiratory syndrome coronavirus 2><death in COVID><death in COVID-19><death in SARS-CoV-2><death in coronavirus disease><death in coronavirus disease 2019><death in first year of life><death in infancy><death in infants><death in severe acute respiratory syndrome coronavirus 2><deprivation><design><designing><developed country><developed nation><developed nations><developmental><disparities affecting females><disparities affecting women><disparities among females><disparities among women><disparities facing women><disparities impacting women><disparities in females><disparities in race><disparities in women><disparity due to race><disparity in ethnic><disparity in health><disparity reduction><economic><ethnic based disparity><ethnic disadvantage><ethnic disparities in health><ethnic disparity><ethnic health disparity><ethnic inequality><ethnic inequity><ethnic minority><ethnic subgroup><ethnicity disparity><ethnicity group><fatality due to COVID-19><fatality due to COVID19><fatality due to SARS-CoV-2><fatality due to coronavirus disease 2019><fatality due to severe acute respiratory syndrome coronavirus 2><female disparities><female reproductive health><geographic site><health care policy><health disparities affecting women><health disparities among females><health disparities among women><health disparities experienced by women><health disparities faced by women><health disparities facing women><health disparities impacting women><health disparities in females><health disparities in women><health disparities that disproportionately affect women><health disparity><health inequalities><health related behavior><healthcare policy><improved><indexing><inequality due to race><inequity due to race><infant death><infant demise><infantile death><innovate><innovation><innovative><interventional strategy><low SES><low socio-economic position><low socio-economic status><low socioeconomic position><low socioeconomic status><machine based learning><machine learning based method><machine learning method><machine learning methodologies><marginalized group><marginalized individual><marginalized people><marginalized population><maternal death><methods to study multiple-level influences><mitigate disparity><mortality><mortality due to COVID-19><mortality due to COVID19><mortality due to SARS-CoV-2><mortality due to coronavirus disease 2019><mortality due to severe acute respiratory syndrome coronavirus 2><mortality in infants><multi-level analysis><multi-level model><multilevel analysis><multilevel model><multilevel modeling><novel><ontogeny><perinatal health><perinatal outcomes><race based disparity><race based inequality><race based inequity><race disparity><race related disparity><race related inequality><race related inequity><racial><racial background><racial disparities in health><racial disparity><racial health disparity><racial inequality><racial inequity><racial minority><racial origin><racial population><racial subgroup><racially unequal><reduce disparity><reduction in disparity><reproductive><reproductive health among females><reproductive health among women><reproductive health in females><reproductive health in women><rural area><rural location><rural region><rurality><severe acute respiratory syndrome coronavirus 2 associated death><severe acute respiratory syndrome coronavirus 2 associated fatality><severe acute respiratory syndrome coronavirus 2 associated mortality><severe acute respiratory syndrome coronavirus 2 death><severe acute respiratory syndrome coronavirus 2 fatality><severe acute respiratory syndrome coronavirus 2 induced death><severe acute respiratory syndrome coronavirus 2 induced fatality><severe acute respiratory syndrome coronavirus 2 induced mortality><severe acute respiratory syndrome coronavirus 2 mortality><severe acute respiratory syndrome coronavirus 2 related death><severe acute respiratory syndrome coronavirus 2 related fatality><severe acute respiratory syndrome coronavirus 2 related mortality><social><social determinants><social factors><social health determinants><social role><socio-economic disparity><socio-economic inequality><socio-economic inequity><socio-economic position><sociodeterminant><socioeconomic disparity><socioeconomic inequality><socioeconomic inequity><socioeconomic position><structural determinants><structural factors><tool><women's disparities><women's health disparities><women's reproductive health>